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A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty

View ORCID ProfileAlfredo Sánchez-Tójar, View ORCID ProfileJulia Schroeder, View ORCID ProfileDamien R. Farine
doi: https://doi.org/10.1101/111146
Alfredo Sánchez-Tójar
aEvolutionary Biology, Max Planck Institute for Ornithology, Seewiesen, Germany
bDepartment of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK
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  • For correspondence: alfredo.tojar@gmail.com dfarine@orn.mpg.de
Julia Schroeder
aEvolutionary Biology, Max Planck Institute for Ornithology, Seewiesen, Germany
bDepartment of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK
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Damien R. Farine
cDepartment of Collective Behaviour, Max Planck Institute for Ornithology, Konstanz, Germany
dDepartment of Biology, University of Konstanz, Germany
eEdward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, UK
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  • For correspondence: alfredo.tojar@gmail.com dfarine@orn.mpg.de
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Abstract

Many animal social structures are organized hierarchically, with dominant individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. As a result, there are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. In this study, we simulated interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and hierarchy steepness affect the performance of three methods, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty in the inferred hierarchy. First, we found that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the hierarchy steepness and method used. We then show that David’s score and our novel randomized Elo-rating are the two best methods, whereas the original Elo-rating and the recently described ADAGIO perform less well. Finally, we propose two simple methods to estimate uncertainty at the individual and group level. These uncertainty measures further allow to differentiate non-existent, very flat and highly uncertain hierarchies from intermediate, steep and certain hierarchies. Overall, we find that the methods for inferring dominance hierarchies are relatively robust, even when the ratio of observed interactions to individuals is as low as 10 to 20. However, we suggest that implementing simple procedures for estimating uncertainty will benefit researchers, and quantifying the shape of the dominance hierarchies will provide new insights into the study organisms.

Highlights

  • David’s score and the randomized Elo-rating perform best.

  • Method performance depends on hierarchy steepness and sampling effort.

  • Generally, inferring dominance hierarchies requires relatively few observations.

  • The R package “aniDom” allows easy estimation of hierarchy uncertainty.

  • Hierarchy uncertainty provides insights into the shape of the dominance hierarchy.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 23, 2017.
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A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty
Alfredo Sánchez-Tójar, Julia Schroeder, Damien R. Farine
bioRxiv 111146; doi: https://doi.org/10.1101/111146
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A practical guide for inferring reliable dominance hierarchies and estimating their uncertainty
Alfredo Sánchez-Tójar, Julia Schroeder, Damien R. Farine
bioRxiv 111146; doi: https://doi.org/10.1101/111146

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